Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3678
Missing cells5667
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory620.0 B

Variable types

Text3
Categorical10
Numeric10

Alerts

puja room has constant value "0" Constant
area is highly overall correlated with bathroom and 5 other fieldsHigh correlation
bathroom is highly overall correlated with area and 5 other fieldsHigh correlation
bedRoom is highly overall correlated with area and 5 other fieldsHigh correlation
built_up_area is highly overall correlated with area and 3 other fieldsHigh correlation
carpet_area is highly overall correlated with area and 5 other fieldsHigh correlation
price is highly overall correlated with area and 7 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
property_type is highly overall correlated with bedRoom and 2 other fieldsHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
super_built_up_area is highly overall correlated with area and 7 other fieldsHigh correlation
store room is highly imbalanced (55.7%) Imbalance
super_built_up_area has 1802 (49.0%) missing values Missing
built_up_area has 1988 (54.1%) missing values Missing
carpet_area has 1806 (49.1%) missing values Missing
area is highly skewed (γ1 = 29.73502026) Skewed
built_up_area is highly skewed (γ1 = 40.70646407) Skewed
carpet_area is highly skewed (γ1 = 24.33323909) Skewed
floorNum has 129 (3.5%) zeros Zeros
luxury_score has 462 (12.6%) zeros Zeros

Reproduction

Analysis started2025-06-30 06:26:12.453006
Analysis finished2025-06-30 06:26:18.154400
Duration5.7 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size394.3 KiB
2025-06-30T11:56:18.253294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length39
Mean length16.869459
Min length1

Characters and Unicode

Total characters62029
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowparas dews
2nd rowrailway officers rpf society
3rd rowumang winter hills
4th rowgodrej nature plus
5th rowdlf the ultima
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.5%
heights 135
 
1.4%
Other values (783) 7498
77.5%
2025-06-30T11:56:18.448528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6711
 
10.8%
6004
 
9.7%
a 5862
 
9.5%
r 4172
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18391
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55480
89.4%
Space Separator 6004
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6711
12.1%
a 5862
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3474
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15358
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
0 13
 
2.5%
9 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6004
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55480
89.4%
Common 6549
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6711
12.1%
a 5862
 
10.6%
r 4172
 
7.5%
n 4164
 
7.5%
i 3832
 
6.9%
t 3721
 
6.7%
s 3474
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15358
27.7%
Common
ValueCountFrequency (%)
6004
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
0 13
 
0.2%
9 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6711
 
10.8%
6004
 
9.7%
a 5862
 
9.5%
r 4172
 
6.7%
n 4164
 
6.7%
i 3832
 
6.2%
t 3721
 
6.0%
s 3474
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18391
29.6%

property_type
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size349.0 KiB
flat
2819 
house
859 

Length

Max length5
Median length4
Mean length4.2335508
Min length4

Characters and Unicode

Total characters15571
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowflat

Common Values

ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Length

2025-06-30T11:56:18.515800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:18.556205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2819
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15571
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15571
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2819
18.1%
l 2819
18.1%
a 2819
18.1%
t 2819
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

sector
Text

Distinct116
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size368.3 KiB
2025-06-30T11:56:18.635507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length26
Median length9
Mean length9.60087
Min length3

Characters and Unicode

Total characters35312
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 106
2nd rowsector 9a
3rd rowsector 77
4th rowsector 33 road
5th rowsector 81
ValueCountFrequency (%)
sector 3451
45.4%
road 385
 
5.1%
sohna 165
 
2.2%
85 108
 
1.4%
102 107
 
1.4%
92 100
 
1.3%
69 93
 
1.2%
90 89
 
1.2%
65 87
 
1.1%
81 87
 
1.1%
Other values (107) 2922
38.5%
2025-06-30T11:56:18.777112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4012
11.4%
3916
11.1%
r 3902
11.1%
s 3694
10.5%
e 3549
10.1%
c 3502
9.9%
t 3462
9.8%
1 1074
 
3.0%
a 903
 
2.6%
0 802
 
2.3%
Other values (21) 6496
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24136
68.4%
Decimal Number 7260
 
20.6%
Space Separator 3916
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 4012
16.6%
r 3902
16.2%
s 3694
15.3%
e 3549
14.7%
c 3502
14.5%
t 3462
14.3%
a 903
 
3.7%
d 456
 
1.9%
n 228
 
0.9%
h 202
 
0.8%
Other values (10) 226
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1074
14.8%
0 802
11.0%
8 779
10.7%
9 761
10.5%
6 739
10.2%
7 684
9.4%
2 680
9.4%
3 666
9.2%
5 592
8.2%
4 483
6.7%
Space Separator
ValueCountFrequency (%)
3916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24136
68.4%
Common 11176
31.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 4012
16.6%
r 3902
16.2%
s 3694
15.3%
e 3549
14.7%
c 3502
14.5%
t 3462
14.3%
a 903
 
3.7%
d 456
 
1.9%
n 228
 
0.9%
h 202
 
0.8%
Other values (10) 226
 
0.9%
Common
ValueCountFrequency (%)
3916
35.0%
1 1074
 
9.6%
0 802
 
7.2%
8 779
 
7.0%
9 761
 
6.8%
6 739
 
6.6%
7 684
 
6.1%
2 680
 
6.1%
3 666
 
6.0%
5 592
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 4012
11.4%
3916
11.1%
r 3902
11.1%
s 3694
10.5%
e 3549
10.1%
c 3502
9.9%
t 3462
9.8%
1 1074
 
3.0%
a 903
 
2.6%
0 802
 
2.3%
Other values (21) 6496
18.4%

price
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.533204
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:18.842987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9803462
Coefficient of variation (CV)1.1765125
Kurtosis14.937194
Mean2.533204
Median Absolute Deviation (MAD)0.72
Skewness3.2796031
Sum9274.06
Variance8.8824634
MonotonicityNot monotonic
2025-06-30T11:56:18.899369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.5%
0.95 52
 
1.4%
2 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3059
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

High correlation 

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13890.391
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:18.955871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4716
Q16815
median9020
Q313878
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063

Descriptive statistics

Standard deviation23207.305
Coefficient of variation (CV)1.6707453
Kurtosis186.97094
Mean13890.391
Median Absolute Deviation (MAD)2795
Skewness11.438429
Sum50852722
Variance5.3857902 × 108
MonotonicityNot monotonic
2025-06-30T11:56:19.011061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3510
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

High correlation  Skewed 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2887.9601
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:19.085500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile519
Q11233
median1733
Q32300
95-th percentile4246
Maximum875000
Range874950
Interquartile range (IQR)1067

Descriptive statistics

Standard deviation23164.352
Coefficient of variation (CV)8.0210082
Kurtosis942.28701
Mean2887.9601
Median Absolute Deviation (MAD)533
Skewness29.73502
Sum10572822
Variance5.3658719 × 108
MonotonicityNot monotonic
2025-06-30T11:56:19.149415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
3240 43
 
1.2%
1950 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3268
88.9%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%
Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size528.6 KiB
2025-06-30T11:56:19.273102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length124
Median length119
Mean length54.231648
Min length12

Characters and Unicode

Total characters199464
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowSuper Built up area 1665(154.68 sq.m.)Built Up area: 1145 sq.ft. (106.37 sq.m.)Carpet area: 1034 sq.ft. (96.06 sq.m.)
2nd rowCarpet area: 1806 (167.78 sq.m.)
3rd rowSuper Built up area 1342(124.68 sq.m.)
4th rowSuper Built up area 1557(144.65 sq.m.)
5th rowSuper Built up area 2132(198.07 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)
ValueCountFrequency (%)
area 5574
18.5%
sq.m 3656
12.1%
up 3021
 
10.0%
built 2317
 
7.7%
super 1876
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8701
28.9%
2025-06-30T11:56:19.475757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82775
41.5%
Decimal Number 47144
23.6%
Space Separator 26469
 
13.3%
Other Punctuation 23409
 
11.7%
Uppercase Letter 8595
 
4.3%
Close Punctuation 5536
 
2.8%
Open Punctuation 5536
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13156
15.9%
r 9458
11.4%
e 9322
11.3%
s 7568
9.1%
q 7432
9.0%
t 7325
8.8%
u 6773
8.2%
p 6769
8.2%
m 5545
6.7%
l 3702
 
4.5%
Other values (5) 5725
6.9%
Decimal Number
ValueCountFrequency (%)
1 9207
19.5%
0 6629
14.1%
2 5690
12.1%
5 4716
10.0%
3 3961
8.4%
4 3712
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3021
35.1%
S 1876
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20392
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26469
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5536
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108094
54.2%
Latin 91370
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13156
14.4%
r 9458
10.4%
e 9322
10.2%
s 7568
8.3%
q 7432
8.1%
t 7325
8.0%
u 6773
7.4%
p 6769
7.4%
m 5545
 
6.1%
l 3702
 
4.1%
Other values (10) 14320
15.7%
Common
ValueCountFrequency (%)
26469
24.5%
. 20392
18.9%
1 9207
 
8.5%
0 6629
 
6.1%
2 5690
 
5.3%
) 5536
 
5.1%
( 5536
 
5.1%
5 4716
 
4.4%
3 3961
 
3.7%
4 3712
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26469
 
13.3%
. 20392
 
10.2%
a 13156
 
6.6%
r 9458
 
4.7%
e 9322
 
4.7%
1 9207
 
4.6%
s 7568
 
3.8%
q 7432
 
3.7%
t 7325
 
3.7%
u 6773
 
3.4%
Other values (25) 82362
41.3%

bedRoom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3597064
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:19.580744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8975034
Coefficient of variation (CV)0.5647825
Kurtosis18.215499
Mean3.3597064
Median Absolute Deviation (MAD)1
Skewness3.4853698
Sum12357
Variance3.600519
MonotonicityNot monotonic
2025-06-30T11:56:19.631729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 943
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 943
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4241436
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:19.682218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9479448
Coefficient of variation (CV)0.56888526
Kurtosis17.544566
Mean3.4241436
Median Absolute Deviation (MAD)1
Skewness3.2490529
Sum12594
Variance3.794489
MonotonicityNot monotonic
2025-06-30T11:56:19.740625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1048
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1048
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size338.5 KiB
3+
1172 
3
1075 
2
884 
1
365 
0
182 

Length

Max length2
Median length1
Mean length1.3186514
Min length1

Characters and Unicode

Total characters4850
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3+
5th row3+

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1075
29.2%
2 884
24.0%
1 365
 
9.9%
0 182
 
4.9%

Length

2025-06-30T11:56:19.796376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:19.846646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%

Most occurring characters

ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
75.8%
Math Symbol 1172
 
24.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2247
61.1%
2 884
 
24.0%
1 365
 
9.9%
0 182
 
4.9%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2247
46.3%
+ 1172
24.2%
2 884
 
18.2%
1 365
 
7.5%
0 182
 
3.8%

floorNum
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7991254
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:19.916558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0118654
Coefficient of variation (CV)0.88421157
Kurtosis4.515315
Mean6.7991254
Median Absolute Deviation (MAD)3
Skewness1.6933376
Sum24878
Variance36.142525
MonotonicityNot monotonic
2025-06-30T11:56:19.986993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 180
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size353.4 KiB
NA
1045 
East
623 
North-East
623 
North
387 
West
249 
Other values (4)
751 

Length

Max length10
Median length5
Mean length5.4643828
Min length2

Characters and Unicode

Total characters20098
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth-West
2nd rowNA
3rd rowNA
4th rowNA
5th rowSouth

Common Values

ValueCountFrequency (%)
NA 1045
28.4%
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 194
 
5.3%
South-East 173
 
4.7%
South-West 153
 
4.2%

Length

2025-06-30T11:56:20.083965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:20.463420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1045
28.4%
east 623
16.9%
north-east 623
16.9%
north 387
 
10.5%
west 249
 
6.8%
south 231
 
6.3%
north-west 194
 
5.3%
south-east 173
 
4.7%
south-west 153
 
4.2%

Most occurring characters

ValueCountFrequency (%)
t 3776
18.8%
N 2249
11.2%
s 2015
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1045
 
5.2%
Other values (4) 2306
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13089
65.1%
Uppercase Letter 5866
29.2%
Dash Punctuation 1143
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3776
28.8%
s 2015
15.4%
o 1761
13.5%
h 1761
13.5%
a 1419
 
10.8%
r 1204
 
9.2%
e 596
 
4.6%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
N 2249
38.3%
E 1419
24.2%
A 1045
17.8%
W 596
 
10.2%
S 557
 
9.5%
Dash Punctuation
ValueCountFrequency (%)
- 1143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18955
94.3%
Common 1143
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3776
19.9%
N 2249
11.9%
s 2015
10.6%
o 1761
9.3%
h 1761
9.3%
E 1419
 
7.5%
a 1419
 
7.5%
r 1204
 
6.4%
A 1045
 
5.5%
W 596
 
3.1%
Other values (3) 1710
9.0%
Common
ValueCountFrequency (%)
- 1143
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3776
18.8%
N 2249
11.2%
s 2015
10.0%
o 1761
8.8%
h 1761
8.8%
E 1419
 
7.1%
a 1419
 
7.1%
r 1204
 
6.0%
- 1143
 
5.7%
A 1045
 
5.2%
Other values (4) 2306
11.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size381.8 KiB
Relatively New
1646 
New Property
594 
Moderately Old
563 
Undefined
306 
Old Property
303 

Length

Max length18
Median length14
Mean length13.385536
Min length9

Characters and Unicode

Total characters49232
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRelatively New
2nd rowOld Property
3rd rowNew Property
4th rowNew Property
5th rowRelatively New

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 594
 
16.2%
Moderately Old 563
 
15.3%
Undefined 306
 
8.3%
Old Property 303
 
8.2%
Under Construction 266
 
7.2%

Length

2025-06-30T11:56:20.533494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:20.587561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2240
31.8%
relatively 1646
23.3%
property 897
12.7%
old 866
 
12.3%
moderately 563
 
8.0%
undefined 306
 
4.3%
under 266
 
3.8%
construction 266
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38810
78.8%
Uppercase Letter 7050
 
14.3%
Space Separator 3372
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8433
21.7%
l 4721
12.2%
t 3638
9.4%
y 3106
 
8.0%
r 2889
 
7.4%
d 2307
 
5.9%
w 2240
 
5.8%
i 2218
 
5.7%
a 2209
 
5.7%
o 1992
 
5.1%
Other values (7) 5057
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 2240
31.8%
R 1646
23.3%
P 897
12.7%
O 866
 
12.3%
U 572
 
8.1%
M 563
 
8.0%
C 266
 
3.8%
Space Separator
ValueCountFrequency (%)
3372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45860
93.2%
Common 3372
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8433
18.4%
l 4721
 
10.3%
t 3638
 
7.9%
y 3106
 
6.8%
r 2889
 
6.3%
d 2307
 
5.0%
N 2240
 
4.9%
w 2240
 
4.9%
i 2218
 
4.8%
a 2209
 
4.8%
Other values (14) 11859
25.9%
Common
ValueCountFrequency (%)
3372
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8433
17.1%
l 4721
 
9.6%
t 3638
 
7.4%
3372
 
6.8%
y 3106
 
6.3%
r 2889
 
5.9%
d 2307
 
4.7%
N 2240
 
4.5%
w 2240
 
4.5%
i 2218
 
4.5%
Other values (15) 14068
28.6%

super_built_up_area
Real number (ℝ)

High correlation  Missing 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1924.931
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:20.653872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11478.75
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)736.25

Descriptive statistics

Standard deviation764.0838
Coefficient of variation (CV)0.39694088
Kurtosis10.351351
Mean1924.931
Median Absolute Deviation (MAD)372
Skewness1.8370902
Sum3611170.6
Variance583824.06
MonotonicityNot monotonic
2025-06-30T11:56:20.728967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 37
 
1.0%
1950 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
1900 19
 
0.5%
2408 19
 
0.5%
1350 18
 
0.5%
1930 18
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct644
Distinct (%)38.1%
Missing1988
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2379.666
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:20.788708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile240.45
Q11100
median1650
Q32400
95-th percentile4691
Maximum737147
Range737145
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation17942.894
Coefficient of variation (CV)7.5400894
Kurtosis1667.8644
Mean2379.666
Median Absolute Deviation (MAD)650
Skewness40.706464
Sum4021635.5
Variance3.2194746 × 108
MonotonicityNot monotonic
2025-06-30T11:56:20.856263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 41
 
1.1%
3240 37
 
1.0%
1900 34
 
0.9%
2700 33
 
0.9%
1350 33
 
0.9%
900 28
 
0.8%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
Other values (634) 1387
37.7%
(Missing) 1988
54.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
50 3
0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 5
0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
13500 1
 
< 0.1%
11286 1
 
< 0.1%
9500 1
 
< 0.1%
9000 7
0.2%
8775 1
 
< 0.1%
8286 1
 
< 0.1%
8115.9806 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%

carpet_area
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct733
Distinct (%)39.2%
Missing1806
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:20.919900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2025-06-30T11:56:21.225381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1800 35
 
1.0%
1600 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1450 22
 
0.6%
1000 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1806
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
2972 
1
706 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Length

2025-06-30T11:56:21.331509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.383918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2972
80.8%
1 706
 
19.2%

servant room
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
2350 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Length

2025-06-30T11:56:21.439087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.498586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2350
63.9%
1 1328
36.1%

store room
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
3340 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Length

2025-06-30T11:56:21.554799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.641188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3340
90.8%
1 338
 
9.2%

puja room
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
3678 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3678
100.0%

Length

2025-06-30T11:56:21.706897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.773075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3678
100.0%

Most occurring characters

ValueCountFrequency (%)
0 3678
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3678
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3678
100.0%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
3272 
1
406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

Length

2025-06-30T11:56:21.832125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.874217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3272
89.0%
1 406
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size337.4 KiB
0
2436 
1
1039 
2
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3678
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

Length

2025-06-30T11:56:21.918995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T11:56:21.960756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2436
66.2%
1 1039
28.2%
2 203
 
5.5%

luxury_score
Real number (ℝ)

Zeros 

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.5
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2025-06-30T11:56:22.012188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.057651
Coefficient of variation (CV)0.74206505
Kurtosis-0.87992994
Mean71.5
Median Absolute Deviation (MAD)38
Skewness0.45949394
Sum262977
Variance2815.1144
MonotonicityNot monotonic
2025-06-30T11:56:22.071292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
38 55
 
1.5%
165 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2314
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2025-06-30T11:56:17.442387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.340182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.907087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.356157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.764012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.192677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.619247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.020259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.418856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.019747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.483308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.423219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.950786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.395125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.806461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.235447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.658954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.059230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.461763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.066742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.525532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.515474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.993828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.434456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.849535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.278628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.699864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.101193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.505121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.110081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.564315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.603540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.035483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.469712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.891441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.319572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.737082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.139006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.545693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.155975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.608697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.653608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.080983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.511770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.935197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.363682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.780610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.182859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.591458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.201382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.655118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.697304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.125929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.553939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.979349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.408115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.823318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.223538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.637399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.243996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.695255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.735394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.166748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.592073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.020704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.447920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.860390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.262932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.677503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.281802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.735955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.775537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.207657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.634142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.061657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.488681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.898335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.301134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.890760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.322878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.779244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.818345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.265781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.678675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.106366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.533590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.939951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.336647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.934235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.358476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.822335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:13.862620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.311998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:14.722250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.148766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.575833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:15.978952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.377271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:16.971101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-30T11:56:17.400035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-30T11:56:22.125473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossessionareabalconybathroombedRoombuilt_up_areacarpet_areafacingfloorNumfurnishing_typeluxury_scoreotherspriceprice_per_sqftproperty_typeservant roomstore roomstudy roomsuper_built_up_area
agePossession1.0000.0000.2740.1110.1300.0000.0000.2030.1250.2140.2550.1080.1020.0560.3790.2870.1430.1410.086
area0.0001.0000.0110.6870.6240.8350.8010.0080.1160.0430.2590.0420.7440.2070.0280.0150.0390.0180.948
balcony0.2740.0111.0000.2250.1760.0000.0260.1650.0790.1780.2230.0820.1360.0330.2140.4410.1460.1830.306
bathroom0.1110.6870.2251.0000.8620.4640.5990.077-0.0050.1950.1790.0700.7200.4110.4720.5200.2440.1760.819
bedRoom0.1300.6240.1760.8621.0000.3800.5690.052-0.1040.1670.0570.0790.6810.4170.5950.3170.2230.1540.800
built_up_area0.0000.8350.0000.4640.3801.0000.9690.0000.0910.0900.2890.0000.6050.1320.0000.0000.0000.0000.926
carpet_area0.0000.8010.0260.5990.5690.9691.0000.0210.1590.0000.2390.0160.6130.1360.0000.0000.0000.0030.894
facing0.2030.0080.1650.0770.0520.0000.0211.0000.0260.1950.1740.0760.0450.0120.0800.2880.1360.1250.093
floorNum0.1250.1160.079-0.005-0.1040.0910.1590.0261.0000.0260.2320.0330.001-0.1260.4850.0830.1120.0780.151
furnishing_type0.2140.0430.1780.1950.1670.0900.0000.1950.0261.0000.2380.0630.1750.0220.0850.2670.1560.1370.132
luxury_score0.2550.2590.2230.1790.0570.2890.2390.1740.2320.2381.0000.1760.2150.0550.3280.3470.2280.1820.222
others0.1080.0420.0820.0700.0790.0000.0160.0760.0330.0630.1761.0000.0340.0360.0250.0000.1060.0330.085
price0.1020.7440.1360.7200.6810.6050.6130.0450.0010.1750.2150.0341.0000.7440.5430.3690.3030.2440.773
price_per_sqft0.0560.2070.0330.4110.4170.1320.1360.012-0.1260.0220.0550.0360.7441.0000.2010.0440.0000.0300.287
property_type0.3790.0280.2140.4720.5950.0000.0000.0800.4850.0850.3280.0250.5430.2011.0000.0650.2410.1271.000
servant room0.2870.0150.4410.5200.3170.0000.0000.2880.0830.2670.3470.0000.3690.0440.0651.0000.1610.1840.584
store room0.1430.0390.1460.2440.2230.0000.0000.1360.1120.1560.2280.1060.3030.0000.2410.1611.0000.2260.046
study room0.1410.0180.1830.1760.1540.0000.0030.1250.0780.1370.1820.0330.2440.0300.1270.1840.2261.0000.121
super_built_up_area0.0860.9480.3060.8190.8000.9260.8940.0930.1510.1320.2220.0850.7730.2871.0000.5840.0460.1211.000

Missing values

2025-06-30T11:56:17.889478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T11:56:18.013764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T11:56:18.109085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

societyproperty_typesectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompuja roomothersfurnishing_typeluxury_score
0paras dewsflatsector 1061.2011605.01034.0Super Built up area 1665(154.68 sq.m.)Built Up area: 1145 sq.ft. (106.37 sq.m.)Carpet area: 1034 sq.ft. (96.06 sq.m.)33312.0North-WestRelatively New1665.01145.01034.0100000158
1railway officers rpf societyflatsector 9a1.256921.01806.0Carpet area: 1806 (167.78 sq.m.)4331.0NAOld PropertyNaNNaN1806.001000040
2umang winter hillsflatsector 770.866408.01342.0Super Built up area 1342(124.68 sq.m.)22216.0NANew Property1342.0NaNNaN00000179
3godrej nature plusflatsector 33 road1.358670.01557.0Super Built up area 1557(144.65 sq.m.)323+17.0NANew Property1557.0NaNNaN00000038
4dlf the ultimaflatsector 812.2110365.02132.0Super Built up area 2132(198.07 sq.m.)Carpet area: 1650 sq.ft. (153.29 sq.m.)333+3.0SouthRelatively New2132.0NaN1650.0010002149
5signature global parkflatsohna road road0.547248.0745.0Carpet area: 745 (69.21 sq.m.)2131.0NANew PropertyNaNNaN745.000000065
6tulip leafflatsector 692.0611386.01809.0Super Built up area 1812(168.34 sq.m.)33316.0South-WestRelatively New1812.0NaNNaN00000049
7sare crescent parcflatsector 920.955013.01895.0Built Up area: 1895 (176.05 sq.m.)Carpet area: 1500 sq.ft. (139.35 sq.m.)453+13.0NorthUndefinedNaN1895.01500.00000000
8godrej oasisflatsector 88a1.407567.01850.0Super Built up area 1850(171.87 sq.m.)333+4.0EastRelatively New1850.0NaNNaN00000138
9microtek greenburgflatsector 861.938446.02285.0Super Built up area 2285(212.28 sq.m.)34312.0EastRelatively New2285.0NaNNaN01000072
societyproperty_typesectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompuja roomothersfurnishing_typeluxury_score
3792conscient elevateflatsector 594.2518518.02295.0Super Built up area 2295(213.21 sq.m.)3433.0NAUnder Construction2295.0NaNNaN0000000
3793emaar the palm springshousesector 5424.00600000.0400.0Plot area 400(37.16 sq.m.)5521.0North-EastOld PropertyNaN400.0NaN110001122
3794bestech park view spa nextflatsector 672.7011489.02350.0Super Built up area 2350(218.32 sq.m.)443+9.0South-WestModerately Old2350.0NaNNaN010001149
3795unitech the residencesflatsector 331.109909.01110.0Super Built up area 1110(103.12 sq.m.)Built Up area: 1100 sq.ft. (102.19 sq.m.)Carpet area: 1000 sq.ft. (92.9 sq.m.)2239.0EastModerately Old1110.01100.01000.000000144
3796signature global parkflatsohna road road0.916289.01447.0Carpet area: 1439 (133.69 sq.m.)3331.0WestRelatively NewNaNNaN1439.0000000165
3797m3m woodshireflatsector 1071.157496.01534.0Super Built up area 1534(142.51 sq.m.)Carpet area: 1056 sq.ft. (98.11 sq.m.)2230.0North-EastRelatively New1534.0NaN1056.010000029
3798signature global soleraflatsector 1070.366581.0547.0Carpet area: 547 (50.82 sq.m.)2228.0South-EastRelatively NewNaNNaN547.000000045
3799orris aster court premierflatsector 851.495820.02560.0Super Built up area 2560(237.83 sq.m.)Built Up area: 2017 sq.ft. (187.39 sq.m.)Carpet area: 1890 sq.ft. (175.59 sq.m.)453+10.0EastRelatively New2560.02017.01890.001000184
3800godrej iconflatsector 88a1.318100.01617.0Super Built up area 1617(150.22 sq.m.)223+3.0North-WestRelatively New1617.0NaNNaN100000102
3801independenthousesector 71.3540909.0330.0Built Up area: 330 (30.66 sq.m.)4503.0NAUndefinedNaN330.0NaN0000000